Flow cytometry, for example, is a test method capable of clustering a leukocyte into neutrophils, lymphocytes, monocytes, acidophils, and the like within a short period of time. Leukocyte particle size data obtained by the flow cytometry can be classified into various particle size patterns according to a maturation or disease (see Nonpatent Literature 1).
Many facilities have introduced this test as a daily screening test. However, mostly only clustered numerical data is used and the leukocyte particle size data generated in an analyzer is rarely used for clinical diagnosis. There are various reasons for this. For example, the leukocyte particle size data is huge to the extent that it cannot be handled by an external information system. In addition, raw analysis data is only visual searched and it is difficult to investigate the data by a scientific method.
Considering these, the inventor of the present patent application developed a clustering method based on a self-organizing map (SOP) using leukocyte particle size data obtained as a two-dimensional histogram (see Nonpatent Literatures 2 to 4). This clustering method includes recording leukocyte particle size data in a database, and extracting characteristic patterns using the data mining technique, which enables classification that cannot be made based only on the two-dimensional histogram information.
The conventional classification method is executed in the analyzer by using a fraction separating method with troughs of respective fractions set as boundaries. Each of the resultant fractions is used as one piece of numerical data for a diagnosis. However, in this method, the distribution of a plurality of proximate clusters, e.g., stab cells and segmented cells belonging to neutrophils or normal cells and juvenile cells, cannot be separated.
Nonpatent Literature 1: Noriyuki TATSUMI, Izumi TSUDA, Takayuki TAKUBO et al.: “Practical Use of Automated White Cell Differential”, HORIBA Technical Reports, No. 20, pp. 23-26, 2000.
Nonpatent Literature 2: Hiromi KATAOKA, Hiromi IOKI, Osamu KONISHI, et al.: “Construction of Data Mining Assistance System for Leukocyte Particle Size Distribution”, Japanese Journal of Clinical Laboratory Automation (JJCLA), Vol 27, 4, pp. 583, 2002.
Nonpatent Literature 3: Hiromi KATAOKA, Hiromi IOKI, Osamu KONISHI, et al.: “Clustering and 3D visualization of Leukocyte Scattergrams”, Medical Informatics 22 (Suppl.), pp. 209-210, 2002.
Nonpatent Literature 4: Hiromi IOKI, Hiromi KATAOKA, Yuka KAWASAKI, et al.: “Clustering of Leukocyte Scattergram in Allergic Diseases”, Medical Informatics 22 (Suppl.), pp. 211-212, 2002.